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-rw-r--r--python/src/mteval.pxi141
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+cimport mteval
+
+cdef char* as_str(sentence, error_msg='Cannot convert type %s to str'):
+ cdef bytes ret
+ if isinstance(sentence, unicode):
+ ret = sentence.encode('utf8')
+ elif isinstance(sentence, str):
+ ret = sentence
+ else:
+ raise TypeError(error_msg % type(sentence))
+ return ret
+
+cdef class Candidate:
+ cdef mteval.Candidate* candidate
+ cdef public float score
+
+ property words:
+ def __get__(self):
+ return unicode(GetString(self.candidate.ewords).c_str(), encoding='utf8')
+
+ property fmap:
+ def __get__(self):
+ cdef SparseVector fmap = SparseVector()
+ fmap.vector = new FastSparseVector[weight_t](self.candidate.fmap)
+ return fmap
+
+cdef class SufficientStats:
+ cdef mteval.SufficientStats* stats
+ cdef mteval.EvaluationMetric* metric
+
+ def __dealloc__(self):
+ del self.stats
+
+ property score:
+ def __get__(self):
+ return self.metric.ComputeScore(self.stats[0])
+
+ property detail:
+ def __get__(self):
+ return self.metric.DetailedScore(self.stats[0]).c_str()
+
+ def __len__(self):
+ return self.stats.size()
+
+ def __iter__(self):
+ for i in range(len(self)):
+ yield self.stats[0][i]
+
+ def __iadd__(SufficientStats self, SufficientStats other):
+ self.stats[0] += other.stats[0]
+ return self
+
+ def __add__(SufficientStats x, SufficientStats y):
+ cdef SufficientStats result = SufficientStats()
+ result.stats = new mteval.SufficientStats(mteval.add(x.stats[0], y.stats[0]))
+ result.metric = x.metric
+ return result
+
+cdef class CandidateSet:
+ cdef shared_ptr[mteval.SegmentEvaluator]* scorer
+ cdef mteval.EvaluationMetric* metric
+ cdef mteval.CandidateSet* cs
+
+ def __cinit__(self, SegmentEvaluator evaluator):
+ self.scorer = new shared_ptr[mteval.SegmentEvaluator](evaluator.scorer[0])
+ self.metric = evaluator.metric
+ self.cs = new mteval.CandidateSet()
+
+ def __dealloc__(self):
+ del self.scorer
+ del self.cs
+
+ def __len__(self):
+ return self.cs.size()
+
+ def __getitem__(self, unsigned k):
+ cdef Candidate candidate = Candidate()
+ candidate.candidate = &self.cs[0][k]
+ candidate.score = self.metric.ComputeScore(self.cs[0][k].eval_feats)
+ return candidate
+
+ def __iter__(self):
+ cdef unsigned i
+ for i in range(len(self)):
+ yield self[i]
+
+ def add_kbest(self, Hypergraph hypergraph, unsigned k):
+ self.cs.AddKBestCandidates(hypergraph.hg[0], k, self.scorer.get())
+
+cdef class SegmentEvaluator:
+ cdef shared_ptr[mteval.SegmentEvaluator]* scorer
+ cdef mteval.EvaluationMetric* metric
+
+ def __dealloc__(self):
+ del self.scorer
+
+ def evaluate(self, sentence):
+ cdef vector[WordID] hyp
+ cdef SufficientStats sf = SufficientStats()
+ sf.metric = self.metric
+ sf.stats = new mteval.SufficientStats()
+ ConvertSentence(string(as_str(sentence.strip())), &hyp)
+ self.scorer.get().Evaluate(hyp, sf.stats)
+ return sf
+
+ def candidate_set(self):
+ return CandidateSet(self)
+
+cdef class Scorer:
+ cdef string* name
+
+ def __cinit__(self, char* name):
+ self.name = new string(name)
+
+ def __dealloc__(self):
+ del self.name
+
+ def __call__(self, refs):
+ cdef mteval.EvaluationMetric* metric = mteval.Instance(self.name[0])
+ if isinstance(refs, unicode) or isinstance(refs, str):
+ refs = [refs]
+ cdef vector[vector[WordID]]* refsv = new vector[vector[WordID]]()
+ cdef vector[WordID]* refv
+ cdef bytes ref_str
+ for ref in refs:
+ refv = new vector[WordID]()
+ ConvertSentence(string(as_str(ref.strip())), refv)
+ refsv.push_back(refv[0])
+ del refv
+ cdef unsigned i
+ cdef SegmentEvaluator evaluator = SegmentEvaluator()
+ evaluator.metric = metric
+ evaluator.scorer = new shared_ptr[mteval.SegmentEvaluator](metric.CreateSegmentEvaluator(refsv[0]))
+ del refsv # in theory should not delete but store in SegmentEvaluator
+ return evaluator
+
+ def __str__(self):
+ return self.name.c_str()
+
+BLEU = Scorer('IBM_BLEU')
+TER = Scorer('TER')